EXPLOITING ADVANCED ARTIFICIAL INTELLIGENCE TECHNIQUES FOR MANAGING CLINICAL GUIDELINES: THE GLARE SYSTEM
Paolo Terenziani§, Stefania Montani§, Gianpaolo Molino*, Mauro Torchio*
§DISTA, Univ. del Piemonte Orientale “A. Avogadro”, C.so Borsalino 54, Alessandria, Italy
*Lab. Informatica Clinica, Az. Ospedaliera S. Giovanni Battista, C.so Bramante 88, Torino, Italy GLARE (GuideLine Acquisition, Representation and Execution) is a computer-based system for acquiring, representing and executing clinical guidelines (GL), that heavily exploits advanced Artificial Intelligence (AI) techniques.
First of all, it relies on a high-level knowledge representation language. Since GLARE is meant to be used by physicians, not necessarily having a strong background in computer science and knowledge engineering, when designing such language we aimed at achieving a reasonable compromise between expressiveness and complexity, and made the choice of defining a limited set of clear representation primitives, able to cover most of the relevant aspects of a GL [1; see also 2].
Like many other systems in this area, GLARE has been conceived having in mind the distinction between acquiring and executing a GL. Therefore, it has been implemented as a modular architecture, whose basic components are the acquisition tool and the execution tool. The acquisition tool is able to interact with the expert-physician via a user-friendly graphical interface. It provides various forms of help, including syntactic and semantic tests in order to check the “well-formedness”
of the guidelines being acquired. In particular, extended AI temporal reasoning techniques are used to check the consistency of temporal constraints [1; see also 3]. The execution tool is devoted to run a guideline applied to a specific patient. The patient’s data are automatically retrieved from the hospital database. The tool relies on the use of an agenda, a data structure containing the next action to be executed. This solution grants great flexibility, including the possibility of managing repeated and/or concurrent actions [1]. GLARE’s execution tool distinguishing feature is its ability to support user- physicians in choosing among different alternatives (output of diagnostic and therapeutic decisions) in the guideline. In many cases, these choices should not be taken only on the basis of “local information”, i.e. just considering the decision criteria associated with the specific decision being examined. In fact, also information coming from the paths following the relevant alternatives might be fruitfully used to discriminate. In GLARE, we allow for this “global information” exploitation through the “what if“ facility, which implements a form of hypothetical reasoning enabling users to gather relevant decision parameters (e.g., costs, resources, times) from the selected parts of the guideline in a semi-automatic way. The graphical interface lets the user specify the parameters chosen for the comparison, the starting node of the paths to be compared and (optionally) the ending nodes. Within each path, whenever a decision is reached, the user may select a subset of the alternatives. While resources are simply collected, and costs are summed up, coping with temporal information is a very complex task, which again involves the extension and adaptation of different temporal reasoning techniques developed within the AI community.
GLARE has been successfully tested on clinical guidelines in different domains, including bladder cancer, reflux esophagitis, and heart failure.
References
[1] P. Terenziani, G. Molino, M. Torchio, A Modular Approach for Representing and Executing Clinical Guidelines, AI in Medicine 23 (2001) 249-276.
[2] J. Fox, N. Johns, A. Rahmanzadeh, R. Thomson, Disseminating medical knowledge: the PROforma approach, AI in Medicine 14 (1998) 157-181.
[3] S. Miksch, Y. Shahar, and P. Johnson, Asbru: a task-specific, intention-based, and time-oriented language for representing skeletal plans, in: Proc. 7th Workshop on Knowledge Engineering Methods and Languages (Milton Keynes, UK, 1997) 9-20.
